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Remote Sensing
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Remote Sensing Analysis for Flood Risk
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Remote Sensing Analysis for Flood Risk

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 9053

Special Issue Editors

Dr. Mohammadtaghi Avand

E-Mail Website
Guest Editor
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran
Interests: watershed management; hydrology; flood modelling; remote sensing; bivariate statistics; GIS; machine learning; climate change; land use change; water resources management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Martina Zeleňáková

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Technical University of Košice, Vysokoskolska 4, 042 00 Kosice, Slovakia
Interests: water management; water structures; hydrology; rainwater management; environmental impact assessment; environmental risks
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaguang Li

E-Mail Website
Guest Editor
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Interests: flood mapping; channel morphology; dryland river hydrology; land cover and land use
Special Issues, Collections and Topics in MDPI journals
Dr. Omid Ghorbanzadeh

E-Mail Website
Guest Editor
Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
Interests: object-based image analysis; machine/deep learning; spatial modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the many natural disasters caused by floods, nowadays flood warning and prediction is one of the most important non-structural methods for flood control and hydrology management, agriculture, and reducing the flood risk and damage. In recent years, the increase in population and changes in land use, afforestation, etc., has led to a significant expansion of urban and rural facilities. Additionally, extreme floods occur more frequently in some regions due to ongoing global warming. The hydrological balance, therefore, has been disturbed and has caused the intensification of floods in the watersheds of rivers. In general, preventing the dangers caused by floods, organizing and managing floods in rivers, and finally improving rivers require the identification and determination of flood-prone areas. Space remote sensing (RS) technology can help us identify areas prone to flooding. By using the techniques of RS and geographic information systems (GIS), it is possible to monitor areas with high flood probability, carry out a general assessment of the flood situation in the area, and provide management solutions in the event of a flood. After the end of the flood, by studying repeated images, the time required for water to penetrate the ground, natural drainage, and water evaporation can be monitored to a large extent, and the dynamics of floodplains near rivers can be observed. The most important features of remote sensing that have generated its popularity in flood studies include repeatability, comprehensiveness, and access to past data. Additionally, by using remote-sensing images, data such as digital elevation models (DEM), land use maps, data of past floods, and estimation of flood volume can be prepared.

This Special Issue aims to collect studies and experiences aimed towards aiding and advancing flood monitoring and mapping through remotely sensed data.

The list below provides a general (but not exhaustive) overview of the topics that are encouraged for this Special Issue:

  • Remote-sensing techniques to monitor floods.
  • Flood inundation mapping using high-resolution remote sensing and/or data fusion.
  • The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models.
  • Remote-sensing data for flood hazard and risk mapping.
  • Data assimilation (DA) of remotely sensed data into hydrological and hydraulic models.
  • Improvement in river discretization and monitoring.
  • Estimation of river discharge and parameters (suspended-sediment concentration, particulate organic carbon, etc.).

Dr. Mohammadtaghi Avand
Prof. Dr. Martina Zeleňáková
Dr. Jiaguang Li
Dr. Omid Ghorbanzadeh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • flood monitoring
  • surface water mapping
  • hazard mapping
  • river discharge estimation
  • channel change detection

Published Papers (4 papers)

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Research

18 pages, 6888 KiB  
Article
Sea Level Rise, Land Subsidence, and Flood Disaster Vulnerability Assessment: A Case Study in Medan City, Indonesia
by Jonson Lumban-Gaol, Josaphat Tetuko Sri Sumantyo, Efendy Tambunan, David Situmorang, I Made Oka Guna Antara, Maya Eria Sinurat, Ni Putu Asri Ratna Suhita, Takahiro Osawa and Risti Endriani Arhatin
Remote Sens. 2024, 16(5), 865; https://doi.org/10.3390/rs16050865 - 29 Feb 2024
Viewed by 1140
Abstract
Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and [...] Read more.
Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City. We employ a data-driven approach integrating multicriteria analysis, analytical hierarchy process (AHP)-based weighting, and spatial modeling within a geographic information system framework. The analysis considers crucial factors such as slope, land use, soil type, SLR, and land deformation. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. Future flood-intensity scenarios are simulated based on SLR projections. Data for spatial analysis primarily originated from multisensor satellite imagery, secondary sources from published literature, and field surveys. We validated the consistency of the variable weightings assigned for vulnerability analysis using a consistency ratio threshold (<0.1). Finally, the established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. The ECNS and Medan City areas were classified as very high and highly vulnerable to flooding, respectively. Full article
(This article belongs to the Special Issue Remote Sensing Analysis for Flood Risk)
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16 pages, 16974 KiB  
Article
A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets
by Junfei Liu, Kai Liu and Ming Wang
Remote Sens. 2023, 15(9), 2447; https://doi.org/10.3390/rs15092447 - 06 May 2023
Cited by 4 | Viewed by 1709
Abstract
Identifying floods and flood susceptibility mapping are critical for decision-makers and disaster management. Machine learning and deep learning have emerged as powerful tools for flood prevention, whereas they confront the drawbacks of overfitting and biased prediction due to the difficulty in obtaining real [...] Read more.
Identifying floods and flood susceptibility mapping are critical for decision-makers and disaster management. Machine learning and deep learning have emerged as powerful tools for flood prevention, whereas they confront the drawbacks of overfitting and biased prediction due to the difficulty in obtaining real data. Therefore, this study presents a novel approach for flood susceptibility prediction by integrating ResNet-18 with a 2D hydrological model for global flood susceptibility mapping using remote sensing datasets. The three main contributions of this study are outlined below. First, a new perspective integrating hydrological simulation and deep learning is presented to overcome the inherent drawbacks of deep learning. Second, the model performance is improved through physics-based initialization. Third, the pretrained model achieves better performance than the original model with incomplete training labels. This experiment demonstrates that the physics-based initialized ResNet-18 model achieves satisfactory prediction performance in terms of accuracy and area under the receiver operating characteristic (ROC) curve (0.854 and 0.932, respectively) and is extremely robust according to a sensitivity analysis. Full article
(This article belongs to the Special Issue Remote Sensing Analysis for Flood Risk)
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21 pages, 12974 KiB  
Article
Historical Trend Analysis and Forecasting of Shoreline Change at the Nile Delta Using RS Data and GIS with the DSAS Tool
by Hany F. Abd-Elhamid, Martina Zeleňáková, Jacek Barańczuk, Marcela Bindzarova Gergelova and Mohamed Mahdy
Remote Sens. 2023, 15(7), 1737; https://doi.org/10.3390/rs15071737 - 23 Mar 2023
Cited by 10 | Viewed by 2870
Abstract
Coastal areas are increasingly endangered by climate change and associated sea level rise, which could have serious consequences, such as shoreline erosion and coastal city submergence. The current study aims to conduct a historical trend analysis (HTA) and predict the shoreline changes of [...] Read more.
Coastal areas are increasingly endangered by climate change and associated sea level rise, which could have serious consequences, such as shoreline erosion and coastal city submergence. The current study aims to conduct a historical trend analysis (HTA) and predict the shoreline changes of the Nile Delta coasts. The Digital Shoreline Analysis System (DSAS) software, with the GIS environment, is used for monitoring the shoreline changes using a number of statistical methods (SCE, NSM, EPR, WLR and LRR). Satellite images from 1974 to 2022 were collected and geometrically corrected using supervised classification to detect the shoreline change of the Nile Delta. The GIS was used for detecting and monitoring changes in the shoreline, as well as forecasting future changes in the shoreline for the next 10 and 20 years (2033–2043). The critical sections of the Nile Delta were identified, and a time series analysis of shoreline changes was conducted. For each section, linear equations were established to predict probable changes in the shoreline. Between 1974 and 2022, the shoreline of the Nile Delta moved inland in different directions due to coastal erosion, and predictions indicate that this erosion will continue until both 2033 and 2043, particularly affecting the Rosetta and Damietta sections. The erosion rate ranged between 30–60 and 10–25 m/year at Rosetta and Damietta, respectively, but at Manzala, it ranged between 8–15 m/year. Continued erosion of the Nile Delta shoreline could have severe consequences that could affect the inhabitants, economy, buildings, roads, railways, and ports. These areas need an integrated coastal management strategy which incorporates increasing consciousness, urban development, and the implementation of rules and adaptation plans. The results of the current study and forecasting the shoreline change could help in protecting such areas. Full article
(This article belongs to the Special Issue Remote Sensing Analysis for Flood Risk)
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21 pages, 10885 KiB  
Article
Increasing Global Flood Risk in 2005–2020 from a Multi-Scale Perspective
by Yu Duan, Junnan Xiong, Weiming Cheng, Yi Li, Nan Wang, Gaoyun Shen and Jiawei Yang
Remote Sens. 2022, 14(21), 5551; https://doi.org/10.3390/rs14215551 - 03 Nov 2022
Cited by 6 | Viewed by 2184
Abstract
In the context of global climate change, floods have become one of the major natural disasters affecting the safety of human life, economic construction, and sustainable development. Despite significant improvements in flood risk and exposure modeling in some studies, there is still a [...] Read more.
In the context of global climate change, floods have become one of the major natural disasters affecting the safety of human life, economic construction, and sustainable development. Despite significant improvements in flood risk and exposure modeling in some studies, there is still a lack of evidence on the spatiotemporal distribution patterns associated with flood risk across the globe. Meanwhile, numerous studies mostly explore flood risk distribution patterns based on specific spatial scales, ignoring to some extent the fact that flood risk has different distribution patterns on different scales. Here, on the basis of hazard–vulnerability components quantified using game theory (GT), we proposed a framework for analyzing the spatiotemporal distribution patterns of global flood risk and the influencing factors behind them on multiple scales. The results revealed that global flood risk increased during 2005–2020, with the percentages of high-risk areas being 4.3%, 4.48%, 4.6%, and 5.02%, respectively. There were 11 global risk hotspots, mainly located in areas with high population concentration, high economic density, abundant precipitation, and low elevation. On the national scale, high-risk countries were mainly concentrated in East Asia, South Asia, Central Europe, and Western Europe. In our experiment, developed countries accounted for the majority of the 20 highest risk countries in the world, with Singapore being the highest risk country and El Salvador having the highest positive risk growth rate (growing by 19.05% during 2015–2020). The findings of this study offer much-needed information and reference for academics researching flood risk under climate change. Full article
(This article belongs to the Special Issue Remote Sensing Analysis for Flood Risk)
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